Julian Besag was an outstanding statistical scientist, distinguished for his pioneering work on the statistical theory and analysis of spatial processes, especially conditional lattice systems. His work has been seminal in statistical developments over the last several decades ranging from image analysis to Markov chain Monte Carlo methods. He clarified the role of auto-logistic and auto-normal models as instances of Markov random fields and paved the way for their use in diverse applications. Later work included investigations into the efficacy of nearest neighbour models to accommodate spatial dependence in the analysis of data from agricultural field trials, image restoration from noisy data, and texture generation using lattice models.

翻译：Julian Besag是一位杰出的统计科学家,因其在统计理论和空间过程分析,特别是有条件的固定系统分析方面的开创性工作而获得杰出的成绩。他的工作在过去几十年中在统计发展方面具有开创性,从图像分析到Markov连锁Monte Carlo方法等方方面面。他澄清了自动和自动正常模型作为Markov随机字段的例子的作用,并为在各种应用中使用这些模型铺平了道路。后来的工作包括调查近邻模型在分析农业实地试验数据、从吵闹数据恢复图像以及使用拉蒂斯模型生成纹理方面满足空间依赖的功效。